A key property of living cells is their ability to react to internal or external stimuli with specific biochemical responses. Bacteria have evolved to sense and rapidly adapt to environmental stimuli by changes in gene expression. Molecular details of the stress response networks regulating these responses have been uncovered for many model bacteria. However, we still lack network-level knowledge of these responses across species, which is necessary to obtain a deeper understanding of cellular functions and to best apply results obtained with model bacteria to pathogenic bacterial species that are poorly characterized or cannot be cultured in a laboratory. In this application we focus on two of the common basic building blocks of bacterial stress-response networks: two-component systems and alternative sigma-factor networks. Combining theoretical, computational and experimental approaches, our multi-disciplinary team will explore two important aspects of network organization - feedback loops due to transcriptional autoregulation and co-transcription of network genes in the same operon. The Specific Aims are (SA1) To understand the relationship between co-expression of bacterial genes from a single operon and stochasticity in information processing of the corresponding networks and (SA2) To assess the role of feedback regulation in alternative sigma factor and two-component system networks. For each Specific Aim the research plan will involve three essential components: (1) formulation and analysis of biophysically realistic but analytically tractable models of master-regulation modules of stress-response networks, (2) simulations and analysis of detailed models of particular networks in the model organism Mycobacterium smegmatis, and (3) experimental tests of predictions in M. smegmatis. This will lead to iterative and synergistic feedback between theories/models and experiments. The results of the proposed work are expected to reveal novel evolutionary design principles characterizing relationships between network architecture and dynamical performance across bacterial species, which are essential for the manipulation of naturally occurring networks and for designing synthetic gene circuits.